Meta-learning applications in NLP have yielded improvements on specific tasks [1]}, [2]}, [3]}, [4]}, [5]}, [6]}.
Unsupervised meta-learning has been explored in computer vision [7]}, [8]} and reinforcement learning [9]}. [7]} cluster images using pre-trained embeddings to create tasks.
[11]} meta-learn an unsupervised update rule
in a semi-supervised framework.
[12]} developed the SMLMT approach to unsupervised meta-learning in NLP.
Contemporary work [13]} explored the use of clustering, though focused only on natural language inference tasks.
Curriculum learning [14]} in the context of meta-learning has been unexplored in NLP, prior to this work.
[15]} found unsupervised curriculum to be beneficial for meta-reinforcement learning.
We refer to [16]} for a comprehensive review of meta-learning.
